As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving business environments as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.
An Enterprise Survey is a firm-level survey of a representative sample of an economy's private sector. Firm-level surveys have been conducted since 1998 by different units within the World Bank. Since 2005-2006, most data collection efforts have been centralized within the Enterprise Analysis Unit. The Enterprise Surveys are conducted across all geographic regions and cover small, medium, and large companies. The surveys are administered to a representative sample of firms in the non-agricultural formal private economy. Data are used to create indicators that benchmark the quality of the business and investment climate across countries.
As of December 2019, the ES covers over 180,000 firms in 150 countries, of which 142 have been surveyed following the standard methodology. This allows for better comparisons across countries and across time. Data are used to create statistically significant business environment indicators that are comparable across countries. The ES are also used to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms.
The survey was conducted in Russia between January and July 2019 as joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG).
The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.
Version 01. Edited, anonymous dataset for public distribution.
The 2019 Russian Federation Enterprise Survey covered the following topics:
- General information of a firm/ establishment
- Infrastructure and services
- Sales and supplies
- Management practices
- Degree of competition
- Time use of top manager
- Land and permits
- Business-Government relations
- Business environment
The Russia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
Producers and sponsors
World Bank Group (WBG)
European Bank for Reconstruction and Development (EBRD)
European Investment Bank (EIB)
World Bank Group
European Bank for Reconstruction and Development
European Investment Bank
The sample for 2019 Russia ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.
Three levels of stratification were used in this country: industry, establishment size, and region.
Industry stratification was designed in the way that follows: the universe was stratified into six manufacturing industries and two services industries- Food and Beverages (ISIC Rev. 3.1 code 15), Garments (ISIC code 18), Non- Metallic Mineral Products (ISIC code 26), Fabricated Metal Products (ISIC code 28), Machinery & Equipment (ISIC code 29), Other Manufacturing (ISIC codes 16-17, 19-25, 27, 30-37), Retail (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).
For the Russia ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
Regional stratification for the Russia ES was done across seven regions: Central Federal District, South (combining Southern Federal District and North-Caucasian Federal District), North-West Federal District, Far Eastern Federal District, Siberian Federal District, Ural Federal District and Volga Federal District.
Note: Refer to Sampling Structure section in "The Russia 2019 Enterprise Surveys Data Set" document for further details on sampling.
Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.
Item non-response was addressed by two strategies:
a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don't know (-9).
b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.
The number of interviews per contacted establishments was 26.0%.
Since the sampling design was stratified and employed differential sampling, individual observations should be properly weighted when making inferences about the population. Under stratified random sampling, unweighted estimates are biased unless sample sizes are proportional to the size of each stratum. With stratification the probability of selection of each unit is, in general, not the same. Consequently, individual observations must be weighted by the inverse of their probability of selection (probability weights or pw in Stata.)
Special care was given to the correct computation of the weights. It was imperative to accurately adjust the totals within each region/industry/size stratum to account for the presence of ineligible units (the firm discontinued businesses or was unattainable, education or government establishments, no reply after having called in different days of the week and in different business hours, no tone in the phone line, answering machine, fax line, wrong address or moved away and could not get the new references). The information required for the adjustment was collected in the first stage of the implementation: the screening process. Using this information, each stratum cell of the universe was scaled down by the observed proportion of ineligible units within the cell. Once an accurate estimate of the universe cell (projections) was available, weights were computed using the number of completed interviews.
Dates of Data Collection
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
The survey was implemented following a 2-stage procedure. Typically, first a screener questionnaire is applied over the phone to determine eligibility and to make appointments. Then a face-to-face interview takes place with the Manager/Owner/Director of each establishment. However, sometimes the phone numbers were unavailable in the sample frame, and thus the enumerators applied the screeners in person. Interviews were conducted using Computer-assisted personal interviewing (CAPI) in Russia. The variables a4b and a6c contain the industry and size of the establishment from the screener questionnaire.
The structure of the data base reflects the fact that 2 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.
Enterprise Analysis Unit
World Bank Group
Confidentiality of the survey respondents and the sensitive information they provide is necessary to ensure the greatest degree of survey participation, integrity and confidence in the quality of the data. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but confidentiality is never compromised.
The use of this dataset must be acknowledged using a citation which would include:
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online).
The World Bank. Russian Federation - Enterprise Survey (ES) 2019, Ref. RUS_2019_ES_v01_M. Dataset downloaded from https://www.enterprisesurveys.org/portal/login.aspx on [date].
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
DDI Document ID
Development Economics Data Group
The World Bank
Documentation of the DDI
Date of Metadata Production
DDI Document version
Version 01 (December 2019)